Implementation of three LWC Schemes in the WiFi 4-Way Handshake with Software Defined Radio
Bibliographic record
Abstract
With the rapid deployment of Internet of Things (IoT) devices in applications such as smarthomes, healthcare and industrial automation, security and privacy has become a major concern. Recently, National Institute of Standards and Technology (NIST) has initiated a lightweight cryptography (LWC) competition to standardize new cryptographic algorithm(s) for providing security in resource-constrained environments. In this context, measuring the suitability of new algorithms with existing communication and authentication protocols is an important problem. This paper investigates the performance of three NIST lightweight authenticated ciphers in round 2 namely ACE, SPIX and WAGE in the WiFi and CoAP handshaking authentication protocols. We implement the WiFi and CoAP handshake protocols and the IEEE802.11a physical layer communication protocol in software defined radio (SDR) and embed these two handshaking protocols into the IEEE802.11a OFDM communication protocol to measure the performance of three ciphers. We present the construction of KDF and MIC used in the handshaking authentication protocols and provide optimized implementations of ACE, SPIX and WAGE including KDF and MIC on three different (low-power) microcontrollers. The performance results of these three ciphers when adopted in WiFi and CoAP protocols are presented. Our experimental results show that the cryptographic functionalities are the bottleneck in the handshaking and data protection protocols.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".